Reducing the effect of wave dispersion in a timber pole based on transversely isotropic material modelling

Round timbers are used for telecommunication and power distribution networks, jetties, piles, short span bridges etc. To assess the condition of these cylindrical shape timber structures, bulk and elementary wave theory are usually used. Even though guided wave can represents the actual wave behaviour, a great deal complexity exists to model stress wave propagation within an orthotropic media, such as timber. In this paper, timber is modelled as transversely isotropic material without compromising the accuracy to a great extent. Dispersion curves and mode shapes are used to propose an experimental set up in terms of the input frequency and bandwidth of the signal, the orientation of the sensor and the distance between the sensors in order to reduce the effect of the dispersion in the output signal. Some example based on the simulated signal is also discussed to evaluate the proposed experimental set up.

Separation of longitudinal and flexural wave in a cylindrical structure based on sensor arrangement for non-destructive evaluation

Low strain integrity testing is commonly used to assess the in situ condition of the poles or piles. For poles, it is important to calculate the embedment length and location of damage which is highly influenced by the accurate determination of the wave velocity. In general, depending on impact location and orientation, both longitudinal and bending waves may generate inside the pole, and these two waves have very distinct characteristics and wave velocity. These differences are even more prominent in the low frequency which is usually induced in the low strain non-destructive testing. Consequently, it will be useful if these two waves can be separated for the condition assessment of the poles. In this paper, a numerical analysis is performed on a pole considering that both waves are generated, and a method is proposed to differentiate these two waves based on an appropriate sensor arrangement that includes the location and the orientation of the sensors. Continuous wavelet transform is applied on the numerical signal to calculate the phase velocity of the waves and compared with analytical phase velocity curves. From the results, it can be seen that appropriate location and orientation of the sensors can separate the longitudinal and flexural waves as they match significantly well with the corresponding analytical phase velocity curves of these two waves.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

Fast vision-based catheter 3D reconstruction

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters, where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors can not be employed in the way they are in rigid robotic manipulators. In this paper, a high speed vision-based shape sensing algorithm for real-time 3D reconstruction of continuum robots based on the views of two arbitrary positioned cameras is presented. The algorithm is based on the closed-form analytical solution of the reconstruction of quadratic curves in 3D space from two arbitrary perspective projections. High-speed image processing algorithms are developed for the segmentation and feature extraction from the images. The proposed algorithms are experimentally validated for accuracy by measuring the tip position, length and bending and orientation angles for known circular and elliptical catheter shaped tubes. Sensitivity analysis is also carried out to evaluate the robustness of the algorithm. Experimental results demonstrate good accuracy (maximum errors of  ±0.6 mm and  ±0.5 deg), performance (200 Hz), and robustness (maximum absolute error of 1.74 mm, 3.64 deg for the added noises) of the proposed high speed algorithms.

Identifying diffusion sources in large networks: a community structure based approach

The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, most methods proposed so far are unsuitable for large networks because of their computational cost and the complex spatiotemporal diffusion processes. In this paper, we develop a community structure based approach to efficiently identify diffusion sources in large networks. We first detect the community structure of a network and assign sensors on community bridge nodes to record diffusion dynamics. From the infection time of bridge sensors, we can determine the very first infected community from which the diffusion started and spread out to other communities. This, therefore, overcomes the scalability issue in source identification problems by narrowing the set of suspects down to the first infected community. Then, to accurately locate the diffusion source from suspects, we utilize an intrinsic feature of diffusion sources that the relative infection time of any node is linear with its effective distance from the diffusion source. Thus, for each suspect, we compute the correlation coefficient to measure the degree of linear dependence between sensors' relative infection times and their effective distances from the suspect, and consider the one with the greatest correlation coefficient as the source. We evaluate our approach in two large networks containing more than 300,000 nodes, which are collected from Twitter. The experiment results show that our method can identify diffusion sources with very high degree of accuracy. Especially when the average community size shrinks, the accuracy of our approach increases dramatically.

Strain sensors with adjustable sensitivity by tailoring the microstructure of graphene aerogel/PDMS nanocomposites

Strain sensors with high elastic limit and high sensitivity are required to meet the rising demand for wearable electronics. Here, we present the fabrication of highly sensitive strain sensors based on nanocomposites consisting of graphene aerogel (GA) and polydimethylsiloxane (PDMS), with the primary focus being to tune the sensitivity of the sensors by tailoring the cellular microstructure through controlling the manufacturing processes. The resultant nanocomposite sensors exhibit a high sensitivity with a gauge factor of up to approximately 61.3. Of significant importance is that the sensitivity of the strain sensors can be readily altered by changing the concentration of the precursor (i.e., an aqueous dispersion of graphene oxide) and the freezing temperature used to process the GA. The results reveal that these two parameters control the cell size and cell-wall thickness of the resultant GA, which may be correlated to the observed variations in the sensitivities of the strain sensors. The higher is the concentration of graphene oxide, then the lower is the sensitivity of the resultant nanocomposite strain sensor. Upon increasing the freezing temperature from −196 to −20 °C, the sensitivity increases and reaches a maximum value of 61.3 at −50 °C and then decreases with a further increase in freezing temperature to −20 °C. Furthermore, the strain sensors offer excellent durability and stability, with their piezoresistivities remaining virtually unchanged even after 10 000 cycles of high-strain loading−unloading. These novel findings pave the way to custom design strain sensors with a desirable piezoresistive behavior.

Performance analysis of a field-effect-transistor based aptasensor

Prostate cancer is one of the most diagnosed cancers which leads to a considerable number of deaths due to the lack of early and sensitive detection. This paper presents an aptamer functionalized field effect (FET) based biosensor for the detection of prostate cancer. Prostate specific antigen (PSA) is considered as the biomarker for prostate cancer whose detection is confirmed by attaching aptamers onto the sensor surface. Through the modelling and numerical simulation, the paper aims to evaluate and predict the performance parameters such as sensitivity, settling time, and limit of detection (LOD) of a label-free FET based electronic biosensor. Various sensor parameters such as structure (i.e., geometry), type of the FET (e.g., nanowire FET, spherical FET, ion-selective FET, and magnetic particle) radius of the FET channel and incubation time are optimized and analyzed. In addition, concentration of analyte biomolecules, diffusion coefficients and affinity to the receptor molecules are also investigated to determine the optimize performance parameters.

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

IMPROVED SENSITIVITY OF RESONANT MASS SENSOR BASED ON MICRO TILTING PLATE AND MICRO CANTILEVER

Vapor sensors have been used for many years. Their applications range from detection of toxic gases and dangerous chemicals in industrial environments, the monitoring of landmines and other explosives, to the monitoring of atmospheric conditions. Microelectrical mechanical systems (MEMS) fabrication technologies provide a way to fabricate sensitive devices. One type of MEMS vapor sensors is based on mass changing detection and the sensors have a functional chemical coating for absorbing the chemical vapor of interest. The principle of the resonant mass sensor is that the resonant frequency will experience a large change due to a small mass of gas vapor change. This thesis is trying to build analytical micro-cantilever and micro-tilting plate models, which can make optimization more efficient. Several objectives need to be accomplished: (1) Build an analytical model of MEMS resonant mass sensor based on micro-tilting plate with the effects of air damping. (2) Perform design optimization of micro-tilting plate with a hole in the center. (3) Build an analytical model of MEMS resonant mass sensor based on micro-cantilever with the effects of air damping. (4) Perform design optimization of micro-cantilever by COMSOL. Analytical models of micro-tilting plate with a hole in the center are compared with a COMSOL simulation model and show good agreement. The analytical models have been used to do design optimization that maximizes sensitivity. The micro-cantilever analytical model does not show good agreement with a COMSOL simulation model. To further investigate, the air damping pressures at several points on the micro-cantilever have been compared between analytical model and COMSOL model. The analytical model is inadequate for two reasons. First, the model’s boundary condition assumption is not realistic. Second, the deflection shape of the cantilever changes with the hole size, and the model does not account for this. Design optimization of micro-cantilever is done by COMSOL.

DESIGN AND APPLICATION OF WIRELESS PASSIVE MAGNETOELASTIC RESONANCE AND MAGNETOHARMONIC FORCE SENSORS

The objective of the work described in this dissertation is the development of new wireless passive force monitoring platforms for applications in the medical field, specifically monitoring lower limb prosthetics. The developed sensors consist of stress sensitive, magnetically soft amorphous metallic glass materials. The first technology is based on magnetoelastic resonance. Specifically, when exposed to an AC excitation field along with a constant DC bias field, the magnetoelastic material mechanically vibrates, and may reaches resonance if the field frequency matches the mechanical resonant frequency of the material. The presented work illustrates that an applied loading pins portions of the strip, effectively decreasing the strip length, which results in an increase in the frequency of the resonance. The developed technology is deployed in a prototype lower limb prosthetic sleeve for monitoring forces experienced by the distal end of the residuum. This work also reports on the development of a magnetoharmonic force sensor comprised of the same material. According to the Villari effect, an applied loading to the material results in a change in the permeability of the magnetic sensor which is visualized as an increase in the higher-order harmonic fields of the material. Specifically, by applying a constant low frequency AC field and sweeping the applied DC biasing field, the higher-order harmonic components of the magnetic response can be visualized. This sensor technology was also instrumented onto a lower limb prosthetic for proof of deployment; however, the magnetoharmonic sensor illustrated complications with sensor positioning and a necessity to tailor the interface mechanics between the sensing material and the surface being monitored. The novelty of these two technologies is in their wireless passive nature which allows for long term monitoring over the life time of a given device. Additionally, the developed technologies are low cost. Recommendations for future works include improving the system for real-time monitoring, useful for data collection outside of a clinical setting.

Microstructured polymer optical fibre sensors for opto-acoustic endoscopy

Opto-acoustic imaging is a growing field of research in recent years, providing functional imaging of physiological biomarkers, such as the oxygenation of haemoglobin. Piezo electric transducers are the industry standard detector for ultrasonics, but their limited bandwidth, susceptibility to electromagnetic interference and their inversely proportional sensitivity to size all affect the detector performance. Sensors based on polymer optical fibres (POF) are immune to electromagnetic interference, have lower acoustic impedance and a reduced Young's Modulus compared to silica fibres. Furthermore, POF enables the possibility of a wideband sensor and a size appropriate to endoscopy. Micro-structured POF (mPOF) used in an interferometric detector has been shown to be an order of magnitude more sensitive than silica fibre at 1 MHz and 3 times more sensitive at 10 MHz. We present the first opto-acoustic measurements obtained using a 4.7mm PMMA mPOF Bragg grating with a fibre diameter of 130 μm and present the lateral directivity pattern of a PMMA mPOF FBG ultrasound sensor over a frequency range of 1-50 MHz. We discuss the impact of the pattern with respect to the targeted application and draw conclusions on how to mitigate the problems encountered.

Fuel level sensor based on polymer optical fiber Bragg gratings for aircraft applications

Safety in civil aviation is increasingly important due to the increase in flight routes and their more challenging nature. Like other important systems in aircraft, fuel level monitoring is always a technical challenge. The most frequently used level sensors in aircraft fuel systems are based on capacitive, ultrasonic and electric techniques, however they suffer from intrinsic safety concerns in explosive environments combined with issues relating to reliability and maintainability. In the last few years, optical fiber liquid level sensors (OFLLSs) have been reported to be safe and reliable and present many advantages for aircraft fuel measurement. Different OFLLSs have been developed, such as the pressure type, float type, optical radar type, TIR type and side-leaking type. Amongst these, many types of OFLLSs based on fiber gratings have been demonstrated. However, these sensors have not been commercialized because they exhibit some drawbacks: low sensitivity, limited range, long-term instability, or limited resolution. In addition, any sensors that involve direct interaction of the optical field with the fuel (either by launching light into the fuel tank or via the evanescent field of a fiber-guided mode) must be able to cope with the potential build up of contamination-often bacterial-on the optical surface. In this paper, a fuel level sensor based on microstructured polymer optical fiber Bragg gratings (mPOFBGs), including poly (methyl methacrylate) (PMMA) and TOPAS fibers, embedded in diaphragms is investigated in detail. The mPOFBGs are embedded in two different types of diaphragms and their performance is investigated with aviation fuel for the first time, in contrast to our previous works, where water was used. Our new system exhibits a high performance when compared with other previously published in the literature, making it a potentially useful tool for aircraft fuel monitoring.

Label-free oligonucleotide biosensor based on dual-peak long period fiber grating

We report the simplification and development of biofunctionalization methodology based on one-step 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)-mediated reaction. The dual-peak long period grating (dLPG) has been demonstrated its inherent ultrahigh sensitivity to refractive index (RI), achieving 50-fold improvement in RI sensitivity over a standard LPG sensor used in low RI range. With the simple and efficient immobilization of unmodified oligonucleotides on sensor surface, dLPG-based biosensor has been used to monitor the hybridization of complementary oligonucleotides showing a detectable oligonucleotide concentration of 4 nM with the advantages of label-free, real-time, and ultrahigh sensitivity.

UGI: a multi-dimensional ultrasonic-based interaction approach

We are currently witnessing an era where interaction with computers is no longer limited to conventional methods (i.e. keyboard and mouse). Human Computer Interaction (HCI) as a progressive field of research, has opened up alternatives to the traditional interaction techniques. Embedded Infrared (IR) sensors, Accelerometers and RGBD cameras have become common inputs for devices to recognize gestures and body movements. These sensors are vision based and as a result the devices that incorporate them will be reliant on presence of light. Ultrasonic sensors on the other hand do not suffer this limitation as they utilize properties of sound waves. These sensors however, have been mainly used for distance detection and not with HCI devices. This paper presents our approach in developing a multi-dimensional interaction input method and tool Ultrasonic Gesture-based Interaction (UGI) that utilizes ultrasonic sensors. We demonstrate how these sensors can detect object movements and recognize gestures. We present our approach in building the device and demonstrate sample interactions with it. We have also conducted a user study to evaluate our tool and its distance and micro gesture detection accuracy. This paper reports these results and outlines our future work in the area.